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- W4312794145 abstract "With the advent of online chatting systems, conversation structure modeling has attracted increasing attention and can be beneficial to many downstream tasks such as topic summarization and dialog generation. Existing studies mainly focus on modeling the pairwise or thread-level text semantics in multiple interleaved conversations, and don’t fully utilize the rich semantics such as the dialogue actions and speaker relationships. For this purpose, we propose to leverage broader semantics to better disentangle the conversations. Concretely, we devise a novel deep neural networks to capture the text contents, discourse structures and speaker attention in one framework, to better identify the utterance pairs with the “reply-to” relationships. We evaluate our framework on two datasets, the publicly available Ubuntu IRC dataset and our proposed WeChat& QQ dataset. The results show that our model significantly outperforms the state-of-the-art baselines." @default.
- W4312794145 created "2023-01-05" @default.
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- W4312794145 date "2022-07-01" @default.
- W4312794145 modified "2023-09-23" @default.
- W4312794145 title "Leveraging Multi-aspect Semantics for Conversation Structure Modeling" @default.
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- W4312794145 doi "https://doi.org/10.1109/dsc55868.2022.00073" @default.
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